Results 51 to 60 of about 762,608 (309)
Robust Geometric Metric Learning
2022 30th European Signal Processing Conference (EUSIPCO), 2022 Published in EUSIPCO 2022.
Collas, Antoine +4 more
openaire +3 more sources
, 2020 Code and documentation is available at https://www.github.com/KevinMusgrave/pytorch-metric ...
Musgrave, Kevin +2 more
openaire +2 more sources
Deep Transfer Metric Learning [PDF]
IEEE Transactions on Image Processing, 2016 Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep
Junlin Hu +3 more
openaire +2 more sources
Dealing With Multipositive Unlabeled Learning Combining Metric Learning and Deep Clustering
IEEE Access, 2022 Standard supervised classification methods make the assumption that the training data is fully annotated thus requiring an a-priory labelling process which is both costly and time-consuming.
Amedeo Racanati +2 more
doaj +1 more source
Learning to Rank Using Localized Geometric Mean Metrics
, 2017 Many learning-to-rank (LtR) algorithms focus on query-independent model, in which query and document do not lie in the same feature space, and the rankers rely on the feature ensemble about query-document pair instead of the similarity between query ...
King, Irwin, Lyu, Michael, Su, Yuxin
core +1 more source
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering
, 2019 Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in \emph{zero-shot image retrieval and clustering}(ZSRC) where a good embedding is requested such that the unseen classes can be distinguished
Chen, Binghui, Deng, Weihong
core +1 more source
Learning to Approximate a Bregman Divergence [PDF]
, 2020 Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning.
Castanon, David +4 more
core
A Metric Learning Reality Check [PDF]
, 2020 Visit https://www.github.com/KevinMusgrave/powerful-benchmarker for supplementary material, including the source code, configuration files, log files, and interactive bayesian optimization ...
Ser-Nam Lim +2 more
openaire +2 more sources
Metrics and Continuity in Reinforcement Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 In most practical applications of reinforcement learning, it is untenable to maintain direct estimates for individual states; in continuous-state systems, it is impossible. Instead, researchers often leverage {\em state similarity} (whether explicitly or implicitly) to build models that can generalize well from a limited set of samples.
Lan, Charline Le +2 more
openaire +2 more sources
Single‐cell insights into the role of T cells in B‐cell malignancies
FEBS Letters, EarlyView.Single‐cell technologies have transformed our understanding of T cell–tumor cell interactions in B‐cell malignancies, revealing new T‐cell subsets, functional states, and immune evasion mechanisms. This Review synthesizes these findings, highlighting the roles of T cells in pathogenesis, progression, and therapy response, and underscoring their ...
Laura Llaó‐Cid
wiley +1 more source